Systems and methods for an artificial intelligence engine to optimize a peak performance

ABSTRACT

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 17/739,906 filed May 9, 2022, titled “Systems and Methods forUsing Machine Learning to Control an Electromechanical Device Used forPrehabilitation, Rehabilitation, and/or Exercise”, which is acontinuation of U.S. patent application Ser. No. 17/150,938, filed Jan.15, 2021, titled “Systems and Methods for Using Machine Learning toControl an Electromechanical Device Used for Prehabilitation,Rehabilitation, and/or Exercise”, which is a continuation-in-part ofU.S. patent application Ser. No. 17/021,895, filed Sep. 15, 2020, titled“Telemedicine for Orthopedic Treatment”, which claims priority to andthe benefit of U.S. Provisional Patent Application Ser. No. 62/910,232,filed Oct. 3, 2019, titled “Telemedicine for Orthopedic Treatment”, theentire disclosures of which are hereby incorporated by reference for allpurposes.

This application also claims priority to and the benefit of U.S.Provisional Patent Application Ser. No. 63/216,805, filed Jun. 30, 2021,titled “Systems and Methods for an Artificial Intelligence Engine toOptimize a Peak Performance”, the entire disclosure of which is herebyincorporated by reference for all purposes.

BACKGROUND

Remote medical assistance, or telemedicine, may aid a patient inperforming various aspects of a rehabilitation regimen for a body part.The patient may use a patient interface in communication with anassistant interface for receiving the remote medical assistance viaaudio, visual, and/or audiovisual communications.

SUMMARY

This disclosure relates generally to the fields of remote medicalassistance and machine learning. Machine learning is generally definedas a field of computer science for discovering methodologies,algorithms, heuristics, and the like, whether in hardware, software orboth, for the purpose of enabling computers or applications running oncomputers to learn without being explicitly programmed. Remote medicalassistance, also referred to as, inter alia, remote medicine,telemedicine, telemed, telmed, tel-med, or telehealth, is generallydefined as an at least two-way communication between a healthcareprofessional, provider or providers, such as a physician, physicaltherapist, a nurse, a chiropractor, etc., and a patient, wherein thetwo-way communication uses audio and/or audiovisual and/or othersensorial or perceptive (e.g., tactile, gustatory, haptic,pressure-sensing-based or electromagnetic (e.g., neurostimulatory)communications (e.g., via a computer, a smartphone, or a tablet).

Machine learning works through a variety of mechanisms, includingiteration, optimization, pruning, testing, and the like. For example, amachine learning model may be trained on a set of training data, suchthat the model may be used to process newly or additionally receiveddata to generate sets of predictions and/or classifications for varioususes related to the discovery, investigation and generation of heuristicmethods for the purpose of optimizing or improving a goal or outcome.Further, machine learning may preferably be continual or evencontinuous: The model developed for machine learning can always befurther improved in light of the goals the model is trained to achieve.While machine learning could, in principle, be terminated at some point,then, in that case, the learning aspect would cease.

An aspect of the disclosed embodiments provides a method for performing,by two or more patients, a respective treatment plan with respectivefirst and second exercise apparatuses, the method comprising. The methodcomprises the steps of: receiving first patient data, wherein the firstpatient data includes at least a first patient identifier associatedwith the first patient and a first treatment plan; receiving secondpatient data, wherein the second patient data includes a second patientidentifier associated with the second patient and a second treatmentplan; receiving first measurement data associated with a firstperformance level of the first treatment plan by the first patient;receiving second measurement data associated with a second performancelevel of the second treatment plan by the second patient; determiningdifferential data, wherein the determining is based on a contrast of oneor more of the first and the second measurement data and first andsecond patient data; and generating, based on the differential data, aninstruction to modify an operating state of the treatment planapparatus.

Another aspect of the disclosed embodiments comprises a system forperforming, by two or more patients, exercises with an exerciseapparatus. The system comprises a processing device and an artificialintelligence engine communicatively coupled to the processing device.The system further comprises a memory including instruction that, whenexecuted by the processing device, cause the processing device to:receive first patient data, wherein the first patient data includes atleast a first patient identifier associated with the first patient and afirst treatment plan; receive second patient data, wherein the secondpatient data includes a second patient identifier associated with thesecond patient and a second treatment plan; receive first measurementdata associated with a first performance level of the first treatmentplan by the first patient; receive second measurement data associatedwith a second performance level of the second exercise by the secondpatient; receive second measurement data associated with a secondperformance level of the second treatment plan by the second patient;determine, via the artificial intelligence engine and based on acontrast of one or more of the first and the second measurement data andfirst and second patient data, differential data; and generate, via theartificial intelligence engine and based on the differential data, aninstruction to modify at least one of the first and the secondexercises.

Another aspect of the disclosed embodiments comprises a tangible,non-transitory machine-readable medium storing instructions that, whenexecuted, cause a processing device to perform any of the operations,steps, functions, and/or methods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer-implemented system for managing a treatment plan according tothe principles of the present disclosure.

FIG. 2 generally illustrates a perspective view of an embodiment of anexercise apparatus according to the principles of the presentdisclosure.

FIG. 3 generally illustrates a perspective view of a pedal of theexercise apparatus of FIG. 2 according to the principles of the presentdisclosure.

FIG. 4 generally illustrates a perspective view of a patient using theexercise apparatus of FIG. 2 according to the principles of the presentdisclosure.

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to the principles of thepresent disclosure.

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to the principles ofthe present disclosure.

FIG. 7 generally illustrates an embodiment of an overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe principles of the present disclosure.

FIG. 8 generally illustrates an embodiment of the overview display ofthe assistant interface presenting, in real-time during a telemedicinesession, recommended treatment plans that have changed as a result ofpatient data changing according to the principles of the presentdisclosure.

FIG. 9 is a flow diagram generally illustrating a method for optimizingat least one exercise according to the principles of the presentdisclosure.

FIG. 10 is a flow diagram generally illustrating a method for optimizingat least one exercise according to the principles of the presentdisclosure.

FIG. 11 generally illustrates a computer system according to theprinciples of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . ” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

The term “patient” may refer, without limitation, to an individual, auser, a student, a class participant, a human, a being, a living entity,etc. Both human and veterinary uses are included within the scope ofthis definition.

The term “treatment” may refer, without limitation, to a medicaltreatment, medical consultation for one or more conditions, trainingprogram, treatment for general health, non-medical treatments (e.g.,treatments not necessarily indicated or prescribed by a healthcareprovider, wherein such treatments are for the purpose of at leastbecoming more toned or muscular in appearance, to increase endurance,pliability, and the like), etc.

The term “healthcare service” may refer, without limitation, tohealthcare services associated with one or more conditions for which thepatient desires to maintain privacy, such as, e.g., services associatedwith conditions for which patients may prefer privacy (over conditionssuch as having a broken finger, or having the flu, etc., where privacyis often less important) like erectile dysfunction, sexually transmitteddisease test results or diagnoses, hemorrhoids, ulcerative colitis,irritable bowel syndrome or disorder, Crohn's disease, diseases orconditions related to the genitourinary systems of males, female orother genders, gender reassignment surgery or medications and hormonesprescribed and associated therewith; and/or, neurodegenerative diseases,orthopedic conditions and cancer diagnoses, treatments or conditions,mental health conditions, such as post-traumatic stress disorder,generalized anxiety, depression, bipolar disorder, schizophreniformdisorders, eating disorders, disorders related to paraphilias,borderline personality disorder; and/or, cardiovascular conditioning,physical conditioning, weight lifting, or any other non-necessarymedical treatment; and/or any other suitable mental health condition andany other service where privacy is mandated by law or requested by thepatient.

A “treatment plan” may refer, without limitation, to one or moretreatment protocols, and each treatment protocol may include one or moretreatment sessions. Each treatment session may comprise several sessionperiods, with each session period including a particular exercise fortreating the body part of the patient. For example, a treatment plan forpost-operative rehabilitation after a knee surgery may include aninitial treatment protocol with twice daily stretching sessions for thefirst three (3) days after surgery and a more intensive treatmentprotocol with active exercise sessions performed four (4) times per daystarting two (2) days after surgery. A treatment plan may also includeinformation pertaining to a medical procedure to perform on the patient,a treatment protocol for the patient using an exercise apparatus, a dietregimen for the patient, a medication regimen for the patient, a sleepregimen for the patient, additional regimens, or some combinationthereof.

The terms telemedicine, telehealth, telemed, teletherapeutic, remotemedicine, etc. may be used interchangeably herein.

The term “condition” may be used to refer to a disease, a state or anyother attribute of the user.

The term “remote medical assistance” may refer, without limitation, toremote medicine, telemedicine, telemed, telmed, tel-med, or telehealth,is an at least two-way communication between a healthcare provider orproviders, such as a physician or a physical therapist, and a patientusing audio and/or audiovisual and/or other sensorial or perceptive(e.g., tactile, gustatory, haptic, pressure-sensing-based orelectromagnetic (e.g., neurostimulation) communications (e.g., via acomputer, a smartphone, or a tablet).

The term “healthcare professional” or “healthcare provider” may refer,without limitation, to a medical professional (e.g., such as a doctor, anurse, a therapist, and the like), an exercise professional (e.g., suchas a coach, a trainer, a nutritionist, and the like), or anotherprofessional sharing at least one of medical and exercise attributes(e.g., such as an exercise physiologist, a physical therapist, anoccupational therapist, and the like). As used herein, and withoutlimiting the foregoing, a “healthcare professional” may be a humanbeing, a robot, a virtual assistant, a virtual assistant in virtualand/or augmented reality, or an artificially intelligent entity, suchentity including a software program, integrated software and hardware,or hardware alone; a doctor, physician assistant, nurse, chiropractor,dentist, physical therapist, acupuncturist, physical trainer, coach,personal trainer, neurologist, cardiologist, or the like (the “Fields ofPractice”), and, without limitation, the “healthcare provider” mayfurther refer to any person with a credential, license, degree, or thelike in the field of medicine, physical therapy, rehabilitation,fitness, sports training, any other field relating to or associated withthe Fields of Practice, or the like

The term “anonymization” may refer, without limitation, to the meaningof the term “anonymization” and/or the meaning of the term“anonymisation,” as these may otherwise have different meanings in,e.g., the United States vs. Europe.

The term “anonymous” may refer, without limitation, to an inability totrace or re-identify the patient's identity.

The term “pseudonymization” may refer, without limitation, to themeaning of the term “pseudonymization” and/or the meaning of the term“pseudonymisation,” as these may otherwise have different meanings in,e.g., the United States vs. Europe.

The term “pseudonymous” may refer to an ability to trace or re-identifythe patent identity though a controlled means (e.g., such as via accessby controlling entities to a controlled database), wherein thepseudomyization may have been effected by the use of one or more PrivacyEnhancing Technologies (PETs)).

The term “enhanced reality” may refer, without limitation, to a userexperience comprising one or more of augmented reality, virtual reality,mixed reality, immersive reality, or a combination of the foregoing(e.g., immersive augmented reality, mixed augmented reality, virtual andaugmented immersive reality, and the like).

The term “augmented reality” may refer, without limitation, to aninteractive user experience that provides an enhanced environment thatcombines elements of a real-world environment with computer-generatedcomponents perceivable by the user.

The term “virtual reality” may refer, without limitation, to a simulatedinteractive user experience that provides an enhanced environmentperceivable by the user and wherein such enhanced environment may besimilar to or different from a real-world environment.

The term “mixed reality” may refer, without limitation, to aninteractive user experience that combines aspects of augmented realitywith aspects of virtual reality to provide a mixed reality environmentperceivable by the user.

The term “immersive reality” may refer, without limitation, to asimulated interactive user experienced using virtual and/or augmentedreality images, sounds, and other stimuli to immerse the user, to aspecific extent possible (e.g., partial immersion or total immersion),in the simulated interactive experience. For example, in someembodiments, to the specific extent possible, the user experiences oneor more aspects of the immersive reality as naturally as the usertypically experiences corresponding aspects of the real-world.Additionally, or alternatively, an immersive reality experience mayinclude actors, a narrative component, a theme (e.g., an entertainmenttheme or other suitable theme), and/or other suitable features ofcomponents.

The term “body halo” may refer, without limitation, to a hardwarecomponent or components, wherein such component or components mayinclude one or more platforms, one or more body supports or cages, oneor more chairs or seats, one or more back supports, one or more leg orfoot engaging mechanisms, one or more arm or hand engaging mechanisms,one or more neck or head engaging mechanisms, other suitable hardwarecomponents, or a combination thereof.

The term “enhanced environment” may refer, without limitation, to anenhanced environment in its entirety, at least one aspect of theenhanced environment, more than one aspect of the enhanced environment,or any suitable number of aspects of the enhanced environment.

The term “medical action(s)” may refer, without limitation, to anysuitable action performed by the medical professional (e.g., or thehealthcare professional), and such action or actions may includediagnoses, prescription of treatment plans, prescription of treatmentdevices, and the making, composing and/or executing of appointments,telemedicine sessions, prescriptions or medicines, telephone calls,emails, text messages, and the like.

The terms “correlate,” “correlation,” and the like may refer to anysuitable correlation or correlative relationship, including acorrelation coefficient (e.g., a statistical value indicating an amountof correlation) not equal to zero (i.e., where zero exactly means thereis no statistical correlation whatsoever), or any suitably definedcorrelation coefficient.

As used herein, the term “electronic medical record, “EMR,” “electronichealth record,” and/or “EHR” may refer, without limitation, to a record(e.g., one or more documents, one or more database entries, and like)that includes information about a health history of a patient,individual, user, and the like. For example, the EMR may includeinformation associated with one or more of diagnoses, medicines, tests,allergies, immunizations, treatment plans, any suitable characteristicsassociated with the patient (e.g., patient, individual, user, and thelike), any suitable conditions associated with the patient (e.g.,patient, individual, user, and the like), and the like.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining optimal remote examination procedures, including medicaldiagnostic procedures, non-diagnostic medical procedures, andnon-medical-related interventions, to create an optimal treatment planfor a patient having certain characteristics (e.g., vital-sign or othermeasurements; performance; demographic; psychographic; geographic;diagnostic; measurement- or test-based; medically historic; etiologic;cohort-associative; differentially diagnostic; surgical, physicallytherapeutic, behavioral, pharmacologic and other treatment(s)recommended; etc.) may be a technically challenging problem. Forexample, a multitude of information or data may be considered whendetermining a treatment plan, which may result in inefficiencies andinaccuracies in the treatment plan selection process. In rehabilitativeand non-rehabilitative (e.g., exercise or fitness) setting, some of themultitude of information considered may include characteristics of thepatient such as personal information, performance information, andmeasurement information associated. The personal information or personaldata may be associated with and/or include, e.g., demographic,psychographic or other information, such as an age, a weight, a gender,a height, a body mass index, a medical condition, a familial medicationhistory, an injury, a medical procedure, a medication prescribed, orsome combination thereof. The performance information or performancedata may be associated with and/or include, e.g., an elapsed time ofusing a treatment device or exercise apparatus, an amount of forceexerted on a portion of the treatment device, a range of motion achievedon the treatment device or exercise apparatus, a movement speed of aportion of the treatment device, a duration of use of the treatmentdevice, an indication of a plurality of pain levels using the treatmentdevice, or some combination thereof. The measurement information ormeasurement data may be associated with or include, e.g., a vital sign,a respiration rate, a heartrate, a temperature, a blood pressure, aglucose level or other biomarker, or some combination thereof. It may bedesirable to process and analyze the characteristics of a multitude ofpatients, the treatment plans performed for those patients, and theresults of the treatment plans for those patients.

Further, another technical problem may involve distally treating,training, or communicating with, via a computing device during atelemedicine, telehealth session or exercise related tele-class (e.g.,remote weight lifting or cycling classes), a patient from a locationdifferent than a location at which the patient is located. An additionaltechnical problem is controlling or enabling the control of, from thedifferent location, a treatment device used by the patient at thelocation at which the patient is located. In one example, often after apatient undergoes rehabilitative surgery (e.g., knee surgery), ahealthcare provider may prescribe a treatment device to the patient touse to perform a treatment protocol at their residence or any mobilelocation or temporary domicile. In one example, A trainer, such as acoach, may outline a treatment plan for a training regimen, forcompetitive or non-competitive purposes, where a patient may use atreatment device to perform the treatment protocol in a remote or mobilelocation, such as at a patient's residence or at a training facility.

Additionally, or alternatively, the two or more healthcare professionalsmay treat the patient (e.g., for the same condition, differentconditions, related conditions, and the like). For example, anorthopedic surgeon, a physical therapist, trainer, coach and/or one ormore other healthcare professionals may cooperatively or independenttreat or be responsible for treatment of the patient for the samecondition or a related condition or conditions (i.e., for certaincomorbidities). Such healthcare professionals may be located remotelyfrom the patient and/or one another.

When the healthcare provider is located in a different location from thepatient and the treatment device, it may be technically challenging forthe healthcare provider to monitor the patient's actual progress (asopposed to relying on the patient's word about their progress) using thetreatment device, modify the treatment plan according to the patient'sprogress, adapt the treatment device to the personal characteristics ofthe patient as the patient performs the treatment plan, and the like.For example, when a trainer is located in a different location from apatient using a treatment device, it may be technically challenging forthe trainer to monitor the patient's actual progress (as opposed torelying on the patient's word about their progress) while the patientuses the treatment device, modify the treatment plan according to thepatient's progress, adapt the treatment device to the personalcharacteristics of the patient as the patient performs the treatmentplan, and the like.

Yet another technical problem may include protecting personal healthcareinformation (PHI) associated with the patient. PHI is a type of PersonalIdentifying Information or PII. The PHI or PII may be associated with,for example, a patient using the treatment device to perform variousexercises and/or a patient receiving at least one service associatedwith a treatment. The law or the patient may demand that the patient'sPHI or PII be maintained as anonymous or pseudonymous. Accordingly, thesystems and methods described herein may be configured such that apatient may seek at least one healthcare service associated with atreatment for one or more conditions, while remaining anonymous orpseudonymous.

In some embodiments, the systems and methods described herein may beconfigured to generate and/or receive a patient identifier associatedwith the patient. The patient identifier may include alphanumeric and/orspecial character information (e.g., such as a unique character stringcomprising one or more alphanumeric characters and/or one or morespecial characters), and/or other suitable identifier or identifyinginformation. Additionally, or alternatively, the patient identifier maybe associated with one or more characteristics associated with thepatient. The patient identifier may be associated with physiologicalinformation about the patient, medications currently being taken by thepatient, and the like. The patient identifier may be associated with oneor more of a past, a current and/or an expected performance level of oneor more treatment plans associated with a patient. The systems andmethods described herein may be configured to store, in a centralizeddatabase or other suitable location, the patient identifier. The systemsand methods described herein may be configured to correlate the patientidentifier with the patient.

For example, the systems and methods described herein may be configuredto execute and be controlled by a PET engine that uses one or more PETsthat control access to personally identifiable information (PII)associated with the patient identifier. Controlling access may refer todefining access, enabling access, disabling access, etc. “Access,” asused in the foregoing, and as further explicated below, may furthercomprise means of de-identification or re-identification. In someembodiments, the PET engine may be configured to pseudonymize oranonymize the PII associated with the patient. In some embodiments, thePET engine may enable de-identification and/or re-identification of thePII associated with the patient. PETs, as used by the PET engine herein,may include, without limitation, differential privacy, homomorphicencryption, public key encryption, digital notarization,pseudonymization, pseudonymisation, anonymization, anonymisation,digital rights management, k-anonymity, I-diversity, synthetic datageneration, suppression, generalization, identity management, and theintroduction of noise into existing data or systems. Further, theforegoing may apply in either or both of classical and quantum computingenvironments, or in any mix thereof. In some embodiments, the one ormore PETs may be configured to support aspects of at least one of theHealth Insurance Portability and Accountability Act (HIPAA)requirements, Gramm-Leach-Bliley Act (GLBA) requirements, EuropeanGeneral Data Protection Regulation (GDPR) requirements, other suitablerequirements, or a combination thereof.

In some embodiments, the systems and methods described herein may beconfigured to identify, based on at least one healthcare serviceindicated by the patient, a healthcare provider associated withproviding the at least one healthcare service. The at least onehealthcare service may be included in the patient identifier, indicatedby the patient using a user interface, or otherwise indicated by thepatient.

In some embodiments, the at least one healthcare service may include anyof the healthcare services described herein, any other suitablehealthcare services, or a combination thereof. In some embodiments, thesystems and methods described herein may be configured to identify,based on at least one of the at least one healthcare service and theidentified healthcare provider, relevant information associated with thepatient identifier. The relevant information may correspond to ahealthcare service of cardiovascular-condition-improving cycling fortraining or rehabilitation.

In some embodiments, the systems and methods described herein may beconfigured to receive input from the patient, wherein the inputindicates a selection of an option. For example, the patient may desireto provide further information related to the first electronic medicalrecord to the healthcare provider. The input may be an indication toprovide further information or to make a selection.

In some embodiments, the healthcare provider may generate, for thepatient, a treatment plan corresponding to one or more conditions of thepatient. Typically, the patient may perform, using the treatment device,various aspects of the treatment plan, such as an exercise, to treat oneor more conditions of the patient. For example, the patient may berecovering from an orthopedic surgery, a cardiac surgery, a neurologicalsurgery, a gastrointestinal surgery, a genito-urological surgery, agynecological surgery, or other surgery and may use the treatment deviceto rehabilitate one or more affected portions of the patient's body.Alternatively, the patient may be recovering from a neurological surgeryor a program to treat mental unwellness and may use the treatment deviceto rehabilitate neurological or other mental responses or brainfunctions which have a physical manifestation with regard to one or moredirectly or indirectly affected portions of the patient's body.Alternatively, the patient may be being treated for physical and/ormental conditions associated with post-traumatic stress disorder (PTSD)and may use the treatment device to rehabilitate neurological or othermental responses or brain functions, which have a physicalmanifestation. Further, the patient, while recovering frompost-traumatic stress disorder, may use the treatment device to improvegeneral mental health (e.g., through exercise, goal-oriented activityand achievement, and the like). Alternatively, the patient may be beingtreated for a somatoform disorder associated with PTSD or other trauma,injury, and the like. The patient may use the treatment device torehabilitate neurological or other mental responses or brain functions,which have a physical manifestation and/or other mental manifestation.Such conditions may be referred to as primary conditions (e.g.,conditions for which the patient uses the treatment device to performthe treatment plan). Similarly, the patient may use the treatment deviceto strengthen training aspects of the treatment plan or of any otherstrength training plan.

In some embodiments, during an adaptive telemedicine session, thesystems and methods described herein may be configured to use artificialintelligence and/or machine learning to assign patients to cohorts andto dynamically control one or more treatment devices based on theassignment. The term “adaptive telemedicine” may refer to a telemedicinesession that is dynamically adapted based on one or more factors,criteria, parameters, characteristics, or the like. The one or morefactors, criteria, parameters, characteristics, or the like may pertainto the user (e.g., heartrate, blood pressure, perspiration rate, painlevel, or the like), the treatment device (e.g., pressure, range ofmotion, speed of motor, etc.), details of the treatment plan, and soforth.

In some embodiments, numerous patients may be prescribed numeroustreatment devices because the numerous patients are recovering from thesame medical procedure, suffering from the same injury, and/orperforming the same exercise. The numerous treatment devices may beprovided to the numerous patients. The treatment devices may be used bythe patients to perform treatment plans in their residences, at gyms, atrehabilitative centers, at hospitals, or at any suitable locations,including permanent or temporary domiciles.

In some embodiments, the treatment devices may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, any or each of thepersonal information, the performance information, and the measurementinformation may be collected before, during, and/or after a patientperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment device throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment device may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step or set of steps in the treatment plan. Such atechnique may enable the determination of which steps in the treatmentplan lead to desired results (e.g., improved muscle strength, range ofmotion, etc.) and which steps lead to diminishing returns (e.g.,continuing to exercise after 3 minutes actually delays or harmsrecovery).

Performance data may be collected from the treatment devices and/or anysuitable computing device (e.g., computing devices where personalinformation is entered, such as the interface of the computing devicedescribed herein, a clinician interface, patient interface, and thelike) over time as the patients use the treatment devices to perform thevarious treatment plans. The performance data that may be collected mayinclude the characteristics of the patients, the treatment plansperformed by the patients, the results of the treatment plans, any ofthe data described herein, any other suitable data, or a combinationthereof.

In some embodiments, the performance data may be processed to groupcertain people into cohorts. The people may be grouped by people havingcertain or selected similar characteristics, treatment plans, andresults of performing the treatment plans. For example, athletic peoplehaving no medical conditions who perform a treatment plan (e.g., use thetreatment device for 30 minutes a day 5 times a week for 3 weeks) andwho fully recover may be grouped into a first cohort. Older people whoare classified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Insome embodiments, the artificial intelligence engine may be used toidentify trends and/or patterns and to define new cohorts based onachieving desired results from the treatment plans and machine learningmodels associated therewith may be trained to identify such trendsand/or patterns and to recommend and rank the desirability of the newcohorts. For example, the one or more machine learning models may betrained to receive an input of characteristics of a new patient and tooutput a treatment plan for the patient that results in a desiredresult. The machine learning models may match a pattern between thecharacteristics of the new patient and at least one patient of thepatients included in a particular cohort. When a pattern is matched, themachine learning models may assign the new patient to the particularcohort and select the treatment plan associated with the at least onepatient. The artificial intelligence engine may be configured tocontrol, distally and based on the treatment plan, the treatment devicewhile the new patient uses the treatment device to perform the treatmentplan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment device toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for people in the cohort to which thenew patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespeople having characteristics similar to the now-changed characteristicsas the new patient. For example, a clinically obese patient may loseweight and no longer meet the weight criterion for the initial cohort,result in the patient's being reassigned to a different cohort with adifferent weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment device may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan,while the new patient uses the treatment device to perform the treatmentplan. Such techniques may provide the technical solution of distallycontrolling a treatment device.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for one or more patients. For example, if a patienthas high blood pressure, a particular exercise may not be approved orsuitable for the patient as it may put the patient at unnecessary riskor even induce a hypertensive crisis and, accordingly, that exercise maybe flagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment device to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient. In someembodiments, the artificial intelligence engine may modify the treatmentplan if the monitored data shows the plan to be inappropriate orcounterproductive for the user.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented to one or more patients, during a group telemedicine orgroup telehealth session, to a healthcare provider. The healthcareprovider may select a particular treatment plan for one or more of thepatients to cause that treatment plan to be transmitted to thecollective patients or an individual patient and/or to control, based onthe treatment plans, one or more treatment devices. In some embodiments,to facilitate telehealth or telemedicine applications, including remotediagnoses, determination of treatment plans and rehabilitative and/orpharmacologic prescriptions, the artificial intelligence engine mayreceive and/or operate distally from the patients and the treatmentdevices.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing device of a healthcare provider. Thevideo may also be accompanied by audio, text and other multimediainformation. Real-time may refer to less than or equal to 2 seconds.Real-time may also refer to near real-time, which may be less than 10seconds or any reasonably proximate difference between two differenttimes. Additionally, or alternatively, near real-time may refer to anyinteraction of a sufficiently short time to enable two individuals toengage in a dialogue via such user interface and will generally be lessthan 10 seconds but greater than 2 seconds.

Presenting the treatment plans generated by the artificial intelligenceengine concurrently with a presentation of the patient video may providean enhanced user interface because the healthcare provider may continueto visually and/or otherwise communicate with the patient while alsoreviewing the treatment plans on the same user interface. The enhanceduser interface may improve the healthcare provider's experience usingthe computing device and may encourage the healthcare provider to reusethe user interface. Such a technique may also reduce computing resources(e.g., processing, memory, network) because the healthcare provider doesnot have to switch to another user interface screen to enter a query fora treatment plan to recommend based on the characteristics of thepatient. The artificial intelligence engine may be configured toprovide, dynamically on the fly, the treatment plans and excludedtreatment plans.

In some embodiments, the treatment device may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare provider may adapt, remotely during atelemedicine session, the treatment device to the needs of the patientby causing a control instruction to be transmitted from a server totreatment device. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

A technical problem may occur which relates to the informationpertaining to the patient's medical condition being received indisparate formats. For example, a server may receive the informationpertaining to a medical condition of the patient from one or moresources (e.g., from an electronic medical record (EMR) system,application programming interface (API), or any suitable system that hasinformation pertaining to the medical condition of the patient). Thatis, some sources used by various healthcare providers may be installedon their local computing devices and may use proprietary formats.Accordingly, some embodiments of the present disclosure may use an APIto obtain, via interfaces exposed by APIs used by the sources, theformats used by the sources. In some embodiments, when information isreceived from the sources, the API may map, translate and/or convert theformat used by the sources to a standardized format used by theartificial intelligence engine. Further, the information mapped,translated and/or converted to the standardized format used by theartificial intelligence engine may be stored in a database accessed bythe artificial intelligence engine when performing any of the techniquesdisclosed herein. Using the information mapped, translated and/orconverted to a standardized format may enable the more accuratedetermination of the procedures to perform for the patient and/or abilling sequence.

To that end, the standardized information may enable the generation oftreatment plans and/or billing sequences having a particular formatconfigured to be processed by various applications (e.g., telehealth).For example, applications, such as telehealth applications, may beexecuting on various computing devices of medical professionals and/orpatients. The applications (e.g., standalone or web-based) may beprovided by a server and may be configured to process data according toa format in which the treatment plans are implemented. Accordingly, thedisclosed embodiments may provide a technical solution by (i) receiving,from various sources (e.g., EMR systems), information innon-standardized and/or different formats; (ii) standardizing theinformation; and (iii) generating, based on the standardizedinformation, treatment plans having standardized formats capable ofbeing processed by applications (e.g., telehealth applications)executing on computing devices of medical professional and/or patients.

With reference to the FIGS., FIG. 1 generally illustrates a blockdiagram of a computer-implemented system 10, hereinafter called “thesystem” for managing a treatment plan. Managing the treatment plan mayinclude using an artificial intelligence engine to recommend treatmentplans and/or provide excluded treatment plans that should not berecommended to a patient.

The system 10 also includes a server 30 configured to store (e.g. writeto an associated memory) and to provide data related to managing thetreatment plan. The server 30 may include one or more computers and maytake the form of a distributed and/or virtualized computer or computers.The server 30 also includes a first communication interface 32configured to communicate with the clinician interface 20 via a firstnetwork 34. In some embodiments, the first network 34 may include wiredand/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee,Near-Field Communications (NFC), cellular data network, etc. The server30 includes a first processor 36 and a first machine-readable storagememory 38, which may be called a “memory” for short, holding firstinstructions 40 for performing the various actions of the server 30 forexecution by the first processor 36. The server 30 is configured tostore data regarding the treatment plan. For example, the memory 38includes a system data store 42 configured to hold system data, such asdata pertaining to treatment plans for treating one or more patients.The server 30 is also configured to store patient data, performancedata, or like the like regarding a patient in following a treatmentplan. For example, the memory 38 includes a patient data store 44configured to hold patient data, such as data pertaining to the one ormore patients, including data representing each patient's performancewithin the treatment plan.

Additionally or alternatively, the characteristics (e.g., personal,performance, measurement, etc.) of the people, the treatment plansfollowed by the patients, the level of compliance with the treatmentplans, and the results of the treatment plans may use correlations andother statistical or probabilistic measures to enable the partitioningof or to partition the treatment plans into different patientcohort-equivalent databases in the patient data store 44. For example,the data for a first cohort of first patients having a first similarinjury, a first similar medical condition, a first similar medicalprocedure performed, a first treatment plan followed by the firstpatient, and a first result of the treatment plan may be stored in afirst patient database. The data for a second cohort of second patientshaving a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic or any combination of characteristics may be used toseparate the cohorts of patients. In some embodiments, the differentcohorts of patients may be stored in different partitions or volumes ofthe same database. There is no specific limit to the number of differentcohorts of patients allowed, other than as limited by mathematicalcombinatoric and/or partition theory.

This characteristic data, treatment plan data, and results data may beobtained from numerous treatment apparatuses and/or computing devicesand/or digital storage media over time and stored in the data store 44.The characteristic data, treatment plan data, and results data may becorrelated in the patient-cohort databases in the patient data store 44.The characteristics of the people may include PHI, PII, other personalinformation, performance information, and/or measurement information.

In addition to the historical information about other people stored inthe patient cohort-equivalent databases, real-time or near-real-timeinformation based on the current patient's characteristics about acurrent patient being treated may be stored in an appropriate patientcohort-equivalent database. The characteristics of the patient may bedetermined to match or be similar to the characteristics of anotherperson in a particular cohort (e.g., cohort A) and the patient may beassigned to that cohort.

In some embodiments, the server 30 may execute an artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign people to certain cohorts based on theircharacteristics, select treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control anexercise apparatus 70, among other things. The machine learning models13 may be trained to generate, based on data associated with a diagnosisof users, desired goal of the user(s), initial treatment plans to beperformed by the users on the exercise apparatus 70. For example, themachine learning models 13 may be trained to provide a visual stimulus,audio stimulus, or haptic stimulus.

With reference to FIG. 10 , the server (also referred to herein as aprocessing device) 30 may receive first patient data, wherein the firstpatient data includes at least a first patient identifier associatedwith the first patient and a first treatment plan. For example, theprocessing device may receive a first patient identifier associated witha resistance level of an exercise apparatus, wherein the resistancelevel is defined by a first treatment plan. The processing device 30 mayalso receive second patient data, wherein the second patient dataincludes a second patient identifier associated with the second patientand a second treatment plan. For example, the processing device 30 mayreceive a second patient identifier associated with a resistance levelof an exercise apparatus,

-   -   wherein the resistance level is associated with a second        treatment plan. The first and the second treatment plans may        include characteristics identical to one another or that differ        from one another.

The processing device 30 may also receive first and second measurementdata associated with respective performance levels of the treatmentplans by the respective patients. For example, the processing device 30may receive measurement data associated with a rate of rotation of thepedals for each patient. The measurement data may be from a current orpast treatment plan. In some embodiments, the first and/or secondpatient identifier may be anonymized or pseudonymized. The anonymized orpseudonymized may be completed by the server 30 and/or the AI engine 11,or by cooperation between the server 30 and the AI engine 11. When thepatient identifier comprises more than one characteristic, theanonymization may also apply to a single patient identifier.

The AI engine 11, based on a contrast associated with one or more of thefirst and the second measurement data and first and second patient data,may determine differential data. For example, the AI engine 11 maydetermine differential data associated with a difference between therate of rotation between the first and the second patients. In anotherexample, the AI engine may determine differential data associated with aperformance level of the first or the second patient, wherein thedifferential data includes data which is outside a pre-determinedthreshold rate of rotation (e.g., the contrast between the rotation rateshould not be more than 10 rotations per minute). The differential datamay also be based on a contrast of measurement data associated with anynumber of current, past, and/or anticipated measurement data.

The AI engine 11 may generate, based on the differential data, aninstruction to modify at least one of the first and the secondexercises. For example, if the differential data identifies a rate ofrotation that exceeds the pre-determined threshold rate of rotation, theAI engine 11 may generate instructions to increase and/or decrease theresistance provided by the exercise apparatus or a part thereof to thefirst or the second patient. In response, the AI engine 11, user, and/orserver 30 may control, based on the differential data, at least one ofthe first and the second exercise apparatus. For example, the AI engine11 may instruct the exercise apparatus 70 to increase or decrease aresistance. The controlling may comprise a modification to any number ofoperating states of the exercise. For example, the positions of theexercise apparatus 70 may be adjusted (e.g., become closer or fartherfrom the patient), and/or a resistance, weight, etc. may be modified.

The machine learning models 13 may also be configured, for example, todisplay on a user interface or otherwise inform the user of a goal forthe day, where the goal is dependent upon the generated treatment plan.For example, the machine learning models may be configured to request ameasurement of a vital sign of the user, a respiration rate of the user,a heartrate of the user, a heart rhythm of a user, an oxygen saturationof the user, a sugar level of the user, a composition of blood of theuser, cerebral activity of the user, cognitive activity of the user, alung capacity of the user, a temperature of the user, a blood pressureof the user, an eye movement of the user, a degree of dilation of an eyeof the user, a reaction time, a sound produced by the user, aperspiration rate of the user, an elapsed time of using the exerciseapparatus 70, an amount of force exerted on a portion of the exerciseapparatus 70, a range of motion achieved on the exercise apparatus 70, amovement speed of a portion of the exercise apparatus 70, a pressureexerted on a portion of the exercise apparatus 70, a movementacceleration of a portion of the exercise apparatus 70, a movement jerkof a portion of the exercise apparatus 70, a torque level of a portionof the exercise apparatus 70, and an indication of a plurality of painlevels experienced by the user when using the exercise apparatus 70. Therequested metric may require an input of sensor data or, in someembodiment, may only require manual entry by the user. In someembodiments, the metrics that the machine learning models 13 are trainedto monitor are related to the underlying condition or attribute of theuser. In other embodiments, the metric that the machine learning models13 are trained to monitor are related to an underlying condition of theuser.

The one or more machine learning models 13 may be generated by thetraining engine 9 and may be implemented in computer instructionsexecutable by one or more processing devices of the training engine 9and/or the servers 30. To generate the one or more machine learningmodels 13, the training engine 9 may train the one or more machinelearning models 13. The one or more machine learning models 13 may beused by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other suitable computing device, or acombination thereof. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of the characteristics (e.g.,medical diagnoses, attributes, a measurement of a vital sign of theuser, a respiration rate of the user, a heartrate of the user, a heartrhythm of a user, an oxygen saturation of the user, a sugar level of theuser, a composition of blood of the user, cerebral activity of the user,cognitive activity of the user, a lung capacity of the user, atemperature of the user, a blood pressure of the user, an eye movementof the user, a degree of dilation of an eye of the user, a reactiontime, a sound produced by the user, a perspiration rate of the user, anelapsed time of using the exercise apparatus 70, an amount of forceexerted on a portion of the exercise apparatus 70, a range of motionachieved on the exercise apparatus 70, a movement speed of a portion ofthe exercise apparatus 70, a pressure exerted on a portion of theexercise apparatus 70, a movement acceleration of a portion of theexercise apparatus 70, a movement jerk of a portion of the exerciseapparatus 70, a torque level of a portion of the exercise apparatus 70,an indication of a plurality of pain levels experienced by the user whenusing the exercise apparatus 70, etc.) of the people that used theexercise apparatus 70 to perform treatment plans, the details (e.g.,treatment protocol including exercises, amount of time to perform theexercises, how often to perform the exercises, a schedule of exercises,parameters/configurations/settings of the exercise apparatus 70throughout each step of the treatment plan, etc.) of the treatment plansperformed by the people using the exercise apparatus 70, and the resultsof the treatment plans performed by the people. The one or more machinelearning models 13 may be trained to match patterns of characteristicsof a patient with characteristics of other people in assigned to aparticular cohort. The term “match” may refer to an exact match, acorrelative match, a substantial match, etc. The one or more machinelearning models 13 may be trained to receive the characteristics of apatient as input, map the characteristics to characteristics of peopleassigned to a cohort, and select a treatment plan from that cohort. Theone or more machine learning models 13 may also be trained to control,based on the treatment plan, treatment apparatus 70. The one or moremachine learning models 13 may also be trained to provide one or moretreatment plan options to a healthcare professional to select from andto control the exercise apparatus 70.

Different machine learning models 13 may be trained to recommenddifferent treatment plans for different desired results. For example,one machine learning model may be trained to recommend treatment plansfor most effective recovery, while another machine learning model may betrained to recommend treatment plans based on speed of recovery.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output, and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1 .

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions. The output device 54 may comprise one ormore different display screens presenting various data and/or interfacesor controls for use by the patient. The output device 54 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.). In some embodiments, the patientinterface 50 may include functionality provided by or similar toexisting voice-based assistants such as Siri by Apple, Alexa by Amazon,Google Assistant, or Bixby by Samsung.

As generally illustrated in FIG. 1 , the patient interface 50 includes asecond communication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes an exercise apparatus 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the exercise apparatus 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The exercise apparatus 70 may be any suitablemedical, rehabilitative, therapeutic, etc. apparatus configured to becontrolled distally via another computing device to treat a patientand/or exercise the patient. The exercise apparatus 70 may be anelectromechanical machine including one or more weights, anelectromechanical bicycle, an electromechanical spin-wheel, asmart-mirror, a treadmill, an interactive environment system or thelike. The body part may include, for example, a spine, a hand, a foot, aknee, or a shoulder. The body part may include a part of a joint, abone, or a muscle group, such as one or more vertebrae, a tendon, or aligament. As generally illustrated in FIG. 1 , the exercise apparatus 70includes a controller 72, which may include one or more processors,computer memory, and/or other components. The exercise apparatus 70 alsoincludes a fourth communication interface 74 configured to communicatewith the patient interface 50 via the local communication interface 68.The exercise apparatus 70 also includes one or more internal sensors 76and an actuator 78, such as a motor. The actuator 78 may be used, forexample, for moving the patient's body part and/or for resisting forcesby the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the exercise apparatus 70 such as, for example, aforce a position, a speed, and/or a velocity. In some embodiments, theinternal sensors 76 may include a position sensor configured to measureat least one of a linear motion or an angular motion of a body part ofthe patient. For example, an internal sensor 76 in the form of aposition sensor may measure a distance that the patient is able to movea part of the exercise apparatus 70, where such distance may correspondto a range of motion that the patient's body part is able to achieve. Insome embodiments, the internal sensors 76 may include a force sensorconfigured to measure a force applied by the patient. For example, aninternal sensor 76 in the form of a force sensor may measure a force orweight the patient is able to apply, using a particular body part, tothe exercise apparatus 70.

The system 10 generally illustrated in FIG. 1 also includes anambulation sensor 82, which communicates with the server 30 via thelocal communication interface 68 of the patient interface 50. Theambulation sensor 82 may track and store a number of steps taken by thepatient. In some embodiments, the ambulation sensor 82 may take the formof a wristband, wristwatch, or smart watch. In some embodiments, theambulation sensor 82 may be integrated within a phone, such as asmartphone.

The system 10 generally illustrated in FIG. 1 also includes a goniometer84, which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The goniometer 84 measures anangle of the patient's body part. For example, the goniometer 84 maymeasure the angle of flex of a patient's knee or elbow or shoulder.

The system 10 generally illustrated in FIG. 1 also includes a pressuresensor 86, which communicates with the server 30 via the localcommunication interface 68 of the patient interface 50. The pressuresensor 86 measures an amount of pressure or weight applied by a bodypart of the patient. For example, pressure sensor 86 may measure anamount of force applied by a patient's foot when pedaling a stationarybike.

The system 10 generally illustrated in FIG. 1 also includes asupervisory interface 90 which may be similar or identical to theclinician interface 20. In some embodiments, the supervisory interface90 may have enhanced functionality beyond what is provided on theclinician interface 20. The supervisory interface 90 may be configuredfor use by a person having responsibility for the treatment plan, suchas an orthopedic surgeon.

The system 10 generally illustrated in FIG. 1 also includes a reportinginterface 92 which may be similar or identical to the clinicianinterface 20. In some embodiments, the reporting interface 92 may haveless functionality from what is provided on the clinician interface 20.For example, the reporting interface 92 may not have the ability tomodify a treatment plan. Such a reporting interface 92 may be used, forexample, by a biller to determine the use of the system 10 for billingpurposes. In another example, the reporting interface 92 may not havethe ability to display patient identifiable information, presenting onlypseudonymized data and/or anonymized data for certain data fieldsconcerning a data subject and/or for certain data fields concerning aquasi-identifier of the data subject. Such a reporting interface 92 maybe used, for example, by a researcher to determine various effects of atreatment plan on different patients.

The system 10 includes an assistant interface 94 a healthcareprofessional, such as those described herein, to remotely communicatewith the patient interface 50 and/or the exercise apparatus 70. Suchremote communications may enable the assistant to provide assistance orguidance to a patient using the system 10. More specifically, theassistant interface 94 is configured to communicate a telemedicinesignal 96, 97, 98 a, 98 b, 99 a, 99 b with the patient interface 50 viaa network connection such as, for example, via the first network 34and/or the second network 58. The telemedicine signal 96, 97, 98 a, 98b, 99 a, 99 b comprises one of an audio signal 96, an audiovisual signal97, an interface control signal 98 a for controlling a function of thepatient interface 50, an interface monitor signal 98 b for monitoring astatus of the patient interface 50, an apparatus control signal 99 a forchanging an operating parameter of the exercise apparatus 70, and/or anapparatus monitor signal 99 b for monitoring a status of the exerciseapparatus 70. In some embodiments, each of the control signals 98 a, 99a may be unidirectional, conveying commands from the assistant interface94 to the patient interface 50. In some embodiments, in response tosuccessfully receiving a control signal 98 a, 99 a and/or to communicatesuccessful and/or unsuccessful implementation of the requested controlaction, an acknowledgement message may be sent from the patientinterface 50 to the assistant interface 94. In some embodiments, each ofthe monitor signals 98 b, 99 b may be unidirectional, status-informationcommands from the patient interface 50 to the assistant interface 94. Insome embodiments, an acknowledgement message may be sent from theassistant interface 94 to the patient interface 50 in response tosuccessfully receiving one of the monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the exercise apparatus 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a in response to an apparatus control signal99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b fromthe assistant interface 94.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, thehealthcare professional may cause content from the prerecorded source tobe played on the patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the healthcareprofessional to speak to a patient via the patient interface 50. In someembodiments, assistant input device 22 may be configured to providevoice-based functionalities, with hardware and/or software configured tointerpret spoken instructions by the assistant by using the one or moremicrophones. The assistant input device 22 may include functionalityprovided by or similar to existing voice-based assistants such as Siriby Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. Theassistant input device 22 may include other hardware and/or softwarecomponents. The assistant input device 22 may include one or moregeneral purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the healthcare professional. The assistant display 24 mayinclude graphics, which may be presented by a web-based interface and/orby a computer program or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the healthcare professional. In some embodiments, thesystem 10 may be configured to recognize and react to spoken requests orcommands by the patient. For example, in response to a verbal command bythe patient (which may be given in any one of several differentlanguages), the system 10 may automatically initiate a telemedicine

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with the assistant interface 94 via the firstnetwork 34. In some embodiments, the first network 34 may include alocal area network (LAN), such as an Ethernet network. In someembodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the exercise apparatus 70 may each operate from apatient location geographically separate from a location of theassistant interface 94. For example, the patient interface 50 and theexercise apparatus 70 may be used as part of an in-home rehabilitationsystem, which may be aided remotely by using the assistant interface 94at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as an healthcare professional remotely from anyconventional office infrastructure. Such remote work may be performed,for example, where the assistant interface 94 takes the form of acomputer and/or telephone. This remote work functionality may allow forwork-from-home arrangements that may include part time and/or flexiblework hours for an healthcare professional.

FIGS. 2-3 show an embodiment of an exercise apparatus 70. Morespecifically, FIG. 2 generally illustrates an exercise apparatus 70 inthe form of a stationary cycling machine 100, which may be called astationary bike, for short. The stationary cycling machine 100 includesa set of pedals 102 each attached to a pedal arm 104 for rotation aboutan axle 106. In some embodiments, and as generally illustrated in FIG. 2, the pedals 102 are movable on the pedal arms 104 in order to adjust arange of motion used by the patient in pedaling. For example, the pedalsbeing located inwardly toward the axle 106 corresponds to a smallerrange of motion than when the pedals are located outwardly away from theaxle 106. One or more pressure sensors 86 is attached to or embeddedwithin one or both of the pedals 102 for measuring an amount of forceapplied by the patient on a pedal 102. The pressure sensor 86 maycommunicate wirelessly to the exercise apparatus 70 and/or to thepatient interface 50.

FIG. 4 generally illustrated a person (a patient) using the exerciseapparatus 70 of FIG. 2 , and generally illustrating sensors and variousdata parameters connected to a patient interface 50. The example patientinterface 50 is a tablet computer or smartphone, or a phablet, such asan iPad, an iPhone, an Android device, or a Surface tablet, which isheld manually by the patient. In some other embodiments, the patientinterface 50 may be embedded within or attached to the exerciseapparatus 70. FIG. 4 generally illustrates the patient wearing theambulation sensor 82 on his wrist, with a note showing “STEPS TODAY1355”, indicating that the ambulation sensor 82 has recorded andtransmitted that step count to the patient interface 50. FIG. 4 alsogenerally illustrates the patient wearing the goniometer 84 on his rightknee, with a note showing “KNEE ANGLE 72°”, indicating that thegoniometer 84 is measuring and transmitting that knee angle to thepatient interface 50. FIG. 4 also shows a right side of one of thepedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,”indicating that the right pedal pressure sensor 86 is measuring andtransmitting that force measurement to the patient interface 50. FIG. 4also shows a left side of one of the pedals 102 with a pressure sensor86 showing “FORCE 27 lbs.”, indicating that the left pedal pressuresensor 86 is measuring and transmitting that force measurement to thepatient interface 50. FIG. 4 also shows other patient data, such as anindicator of “SESSION TIME 0:04:13”, indicating that the patient hasbeen using the exercise apparatus 70 for 4 minutes and 13 seconds. Thissession time may be determined by the patient interface 50 based oninformation received from the exercise apparatus 70. FIG. 4 alsogenerally illustrates an indicator showing “PAIN LEVEL 3”. Such a painlevel may be obtained from the patent in response to a solicitation,such as a question, presented upon the patient interface 50.

Additionally or alternatively, one of more remote sensing devices 108may be spaced from the user for remotely detecting vital signs of theuser. The one or more remote sensing devices 108 may include any one ofor a combination of the sensors shown in FIG. 4 attached to the user'sbody or the exercise apparatus 70, but configured to remotely monitorthe desired feedback. For example, the remote sensing devices 108 mayinclude a high-definition camera or an infrared camera connected to orintegrated with analytical software, such as motion-capture software orfacial-recognition software. The remote sensing devices 108 may also beconfigured to detect the location of at least one node, or marker,placed on the user or the exercise apparatus 70, to detect a speed ornumber of repetitions that have been completed by the user. By way ofexample, the remote sensing devices 108 may detect that the a nodeattached to the right knee of the user moves sporadically (e.g. deviatesfrom an expected motion) while the user uses the exercise apparatus 70.Alternatively or additionally, the remote sensing devices 108 may beconfigured to detect the temperature or perspiration, of the user. Insome embodiments, the remote sensing devices 108 and their associatedsoftware are configured to identify a level of strain the user undergoeswhile the user uses the treatment device. For example, the one or moreremote sensing devices 108 may implement facial recognition to detect achange in the physical appearance of the user (e.g., wrinkling of theskin around the user's eyes, clenching of the user's jaw).

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the healthcareprofessional to remotely assist a patient with using the patientinterface 50 and/or the exercise apparatus 70. This remote assistancefunctionality may also be called telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the exercise apparatus 70. The patient profile display 130 maytake the form of a portion or region of the overview display 120, asgenerally illustrated in FIG. 5 , although the patient profile display130 may take other forms, such as a separate screen or a popup window.In some embodiments, the patient profile display 130 may include alimited subset of the patient's biographical information. Morespecifically, the data presented upon the patient profile display 130may depend upon the healthcare professional's need for that information.For example, a healthcare professional that is assisting the patientwith a medical issue may be provided with medical history informationregarding the patient, whereas a technician troubleshooting an issuewith the exercise apparatus 70 may be provided with a much more limitedset of information regarding the patient. The technician, for example,may be given only the patient's name. The patient profile display 130may include pseudonym zed data and/or anonymized data or use any privacyenhancing technology to prevent confidential patient data from beingcommunicated in a way that could violate patient confidentialityrequirements. Such privacy enhancing technologies may enable compliancewith laws, regulations, or other rules of governance such as, but notlimited to, the Health Insurance Portability and Accountability Act(HIPAA), or the General Data Protection Regulation (GDPR), wherein thepatient may be deemed a “data subject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the exercise apparatus 70. Such treatment plan information may belimited to a healthcare professional. For example, a healthcareprofessional assisting the patient with an issue regarding the treatmentregimen may be provided with treatment plan information, whereas atechnician troubleshooting an issue with the exercise apparatus 70 maynot be provided with any information regarding the patient's treatmentplan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the healthcare professional. The one or more recommendedtreatment plans and/or excluded treatment plans may be generated by theartificial intelligence engine 11 of the server 30 and received from theserver 30 in real-time during a telemedicine or telehealth session. Anexample of presenting the one or more recommended treatment plans and/orruled-out treatment plans is described below with reference to FIG. 7 .

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient status display 134 presenting status informationregarding a patient using the exercise apparatus 70. The patient statusdisplay 134 may take the form of a portion or region of the overviewdisplay 120, as generally illustrated in FIG. 5 , although the patientstatus display 134 may take other forms, such as a separate screen or apopup window. The patient status display 134 includes sensor data 136from one or more of the external sensors 82, 84, 86, and/or from one ormore internal sensors 76 of the exercise apparatus 70. In someembodiments, the patient status display 134 may include sensor data fromone or more sensors of one or more wearable devices worn by the patientor spaced from the patient (i.e., the remote sensing devices 108) whileusing the exercise apparatus 70. The one or more wearable devices mayinclude a watch, a bracelet, a necklace, a chest strap, and the like.The one or more wearable devices may be configured to monitor aheartrate, a temperature, a blood pressure, one or more vital signs, andthe like of the patient while the patient is using the exerciseapparatus 70. The one or more remote sensing devices 108 may beconfigured to interact with or communicate with the wearable devices inorder to more particularly identify attributes of the user. In someembodiments, the patient status display 134 may present other data 138regarding the patient, such as last reported pain level, or progresswithin a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the healthcareprofessional/user's need for and/or qualifications to view thatinformation.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a help data display 140 presenting information for thehealthcare professional to use in assisting the patient. The help datadisplay 140 may take the form of a portion or region of the overviewdisplay 120, as generally illustrated in FIG. 5 . The help data display140 may take other forms, such as a separate screen or a popup window.The help data display 140 may include, for example, presenting answersto frequently asked questions regarding use of the patient interface 50and/or the exercise apparatus 70. The help data display 140 may alsoinclude research data or best practices. In some embodiments, the helpdata display 140 may present scripts for answers or explanations inresponse to patient questions. In some embodiments, the help datadisplay 140 may present flow charts or walk-throughs for the healthcareprofessional to use in determining a root cause and/or solution to apatient's problem. In some embodiments, the assistant interface 94 maypresent two or more help data displays 140, which may be the same ordifferent, for simultaneous presentation of help data for use by thehealthcare professional. for example, a first help data display may beused to present a troubleshooting flowchart to determine the source of apatient's problem, and a second help data display may present scriptinformation for the healthcare professional to read to the patient, suchinformation to preferably include directions for the patient to performsome action, which may help to narrow down or solve the problem. In someembodiments, based upon inputs to the troubleshooting flowchart in thefirst help data display, the second help data display may automaticallypopulate with script information.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient interface control 150 presenting informationregarding the patient interface 50, and/or to modify one or moresettings of the patient interface 50. The patient interface control 150may take the form of a portion or region of the overview display 120, asshown in FIG. 5 . The patient interface control 150 may take otherforms, such as a separate screen or a popup window. The patientinterface control 150 may present information communicated to theassistant interface 94 via one or more of the interface monitor signals98 b. As generally illustrated in FIG. 5 , the patient interface control150 includes a display feed 152 of the display presented by the patientinterface 50. In some embodiments, the display feed 152 may include alive copy of the display screen currently being presented to the patientby the patient interface 50. In other words, the display feed 152 maypresent an image of what is presented on a display screen of the patientinterface 50. In some embodiments, the display feed 152 may includeabbreviated information regarding the display screen currently beingpresented by the patient interface 50, such as a screen name or a screennumber. The patient interface control 150 may include a patientinterface setting control 154 for the healthcare professional to adjustor to control one or more settings or aspects of the patient interface50. In some embodiments, the patient interface setting control 154 maycause the assistant interface 94 to generate and/or to transmit aninterface control signal 98 for controlling a function or a setting ofthe patient interface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for thehealthcare professional to remotely view and/or control the patientinterface 50. For example, the patient interface setting control 154 mayenable the healthcare professional to remotely enter text to one or moretext entry fields on the patient interface 50 and/or to remotely controla cursor on the patient interface 50 using a mouse or touchscreen of theassistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the healthcare professional tochange a setting that cannot be changed by the patient. For example, thepatient interface 50 may be precluded from accessing a language settingto prevent a patient from inadvertently switching, on the patientinterface 50, the language used for the displays, whereas the patientinterface setting control 154 may enable the healthcare professional tochange the language setting of the patient interface 50. In anotherexample, the patient interface 50 may not be able to change a font sizesetting to a smaller size in order to prevent a patient frominadvertently switching the font size used for the displays on thepatient interface 50 such that the display would become illegible to thepatient, whereas the patient interface setting control 154 may providefor the healthcare professional to change the font size setting of thepatient interface 50.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an interface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the exercise apparatus 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as generally illustrated in FIG. 5 . The interfacecommunications display 156 may take other forms, such as a separatescreen or a popup window. The interface communications display 156 mayinclude controls for the healthcare professional to remotely modifycommunications with one or more of the other devices 70, 82, 84. Forexample, the healthcare professional may remotely command the patientinterface 50 to reset communications with one of the other devices 70,82, 84, or to establish communications with a new one of the otherdevices 70, 82, 84. This functionality may be used, for example, wherethe patient has a problem with one of the other devices 70, 82, 84, orwhere the patient receives a new or a replacement one of the otherdevices 70, 82, 84.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an apparatus control 160 for the healthcare professional toview and/or to control information regarding the exercise apparatus 70.The apparatus control 160 may take the form of a portion or region ofthe overview display 120, as generally illustrated in FIG. 5 . Theapparatus control 160 may take other forms, such as a separate screen ora popup window. The apparatus control 160 may include an apparatusstatus display 162 with information regarding the current status of theapparatus. The apparatus status display 162 may present informationcommunicated to the assistant interface 94 via one or more of theapparatus monitor signals 99 b. The apparatus status display 162 mayindicate whether the exercise apparatus 70 is currently communicatingwith the patient interface 50. The apparatus status display 162 maypresent other current and/or historical information regarding the statusof the exercise apparatus 70.

The apparatus control 160 may include an apparatus setting control 164for the healthcare professional to adjust or control one or more aspectsof the exercise apparatus 70. The apparatus setting control 164 maycause the assistant interface 94 to generate and/or to transmit anapparatus control signal 99 (e.g. which may be referred to as treatmentplan input) for changing an operating parameter and/or one or morecharacteristics of the exercise apparatus 70, (e.g., a pedal radiussetting, a resistance setting, a target RPM, other suitablecharacteristics of the treatment device 70, or a combination thereof).The apparatus setting control 164 may include a mode button 166 and aposition control 168, which may be used in conjunction for thehealthcare professional to place an actuator 78 of the exerciseapparatus 70 in a manual mode, after which a setting, such as a positionor a speed of the actuator 78, can be changed using the position control168. The mode button 166 may provide for a setting, such as a position,to be toggled between automatic and manual modes. In some embodiments,one or more settings may be adjustable at any time, and without havingan associated auto/manual mode. In some embodiments, the healthcareprofessional may change an operating parameter of the exercise apparatus70, such as a pedal radius setting, while the patient is actively usingthe exercise apparatus 70. Such “on the fly” adjustment may or may notbe available to the patient using the patient interface 50. In someembodiments, the apparatus setting control 164 may allow the healthcareprofessional to change a setting that cannot be changed by the patientusing the patient interface 50. For example, the patient interface 50may be precluded from changing a preconfigured setting, such as a heightor a tilt setting of the exercise apparatus 70, whereas the apparatussetting control 164 may provide for the healthcare professional tochange the height or tilt setting of the exercise apparatus 70.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient communications control 170 for controlling an audioor an audiovisual communications session with the patient interface 50.The communications session with the patient interface 50 may comprise alive feed from the assistant interface 94 for presentation by the outputdevice of the patient interface 50. The live feed may take the form ofan audio feed and/or a video feed. In some embodiments, the patientinterface 50 may be configured to provide two-way audio or audiovisualcommunications with a person using the assistant interface 94.Specifically, the communications session with the patient interface 50may include bidirectional (two-way) video or audiovisual feeds, witheach of the patient interface 50 and the assistant interface 94presenting video of the other one. In some embodiments, the patientinterface 50 may present video from the assistant interface 94, whilethe assistant interface 94 presents only audio or the assistantinterface 94 presents no live audio or visual signal from the patientinterface 50. In some embodiments, the assistant interface 94 maypresent video from the patient interface 50, while the patient interface50 presents only audio or the patient interface 50 presents no liveaudio or visual signal from the assistant interface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as shown in FIG. 5 . The patientcommunications control 170 may take other forms, such as a separatescreen or a popup window. The audio and/or audiovisual communicationsmay be processed and/or directed by the assistant interface 94 and/or byanother device or devices, such as a telephone system, or avideoconferencing system used by the healthcare professional while thehealthcare professional uses the assistant interface 94. Alternativelyor additionally, the audio and/or audiovisual communications may includecommunications with a third party. For example, the system 10 may enablethe healthcare professional to initiate a 3-way conversation regardinguse of a particular piece of hardware or software, with the patient anda subject matter expert, such as a medical professional or a specialist.The example patient communications control 170 generally illustrated inFIG. 5 includes call controls 172 for the healthcare professional to usein managing various aspects of the audio or audiovisual communicationswith the patient. The call controls 172 include a disconnect button 174for the healthcare professional to end the audio or audiovisualcommunications session. The call controls 172 also include a mute button176 to temporarily silence an audio or audiovisual signal from theassistant interface 94. In some embodiments, the call controls 172 mayinclude other features, such as a hold button (not shown). The callcontrols 172 also include one or more record/playback controls 178, suchas record, play, and pause buttons to control, with the patientinterface 50, recording and/or playback of audio and/or video from theteleconference session (e.g., which may be referred to herein as thevirtual conference room). The call controls 172 also include a videofeed display 180 for presenting still and/or video images from thepatient interface 94, and a self-video display 182 showing the currentimage of the healthcare professional using the assistant interface 94.The self-video display 182 may be presented as a picture-in-pictureformat, within a section of the video feed display 180, as generallyillustrated in FIG. 5 . Alternatively or additionally, the self-videodisplay 182 may be presented separately and/or independently from thevideo feed display 180.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a third party communications control 190 for use in conductingaudio and/or audiovisual communications with a third party. The thirdparty communications control 190 may take the form of a portion orregion of the overview display 120, as generally illustrated in FIG. 5 .The third party communications control 190 may take other forms, such asa display on a separate screen or a popup window. The third partycommunications control 190 may include one or more controls, such as acontact list and/or buttons or controls to contact a third partyregarding use of a particular piece of hardware or software, e.g., asubject matter expert, such as a healthcare professional or aspecialist. The third party communications control 190 may includeconference calling capability for the third party to simultaneouslycommunicate with both the healthcare professional via the assistantinterface 94, and with the patient via the patient interface 50. Forexample, the system 10 may provide for the healthcare professional toinitiate a 3-way conversation with the patient and the third party.

FIG. 6 generally illustrates an example block diagram of training amachine learning model 13 to output, based on data 600 pertaining to thepatient, a treatment plan 602 for the patient according to the presentdisclosure. Data pertaining to other patients may be received by theserver 30. The other patients may have used various treatmentapparatuses to perform treatment plans. The data may includecharacteristics of the other patients, the details of the treatmentplans performed by the other patients, and/or the results of performingthe treatment plans (e.g., a percent of recovery of a portion of thepatients' bodies, an amount of recovery of a portion of the patients'bodies, an amount of increase or decrease in muscle strength of aportion of patients' bodies, an amount of increase or decrease in rangeof motion of a portion of patients' bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the exercise apparatus 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the exercise apparatus 70 are set toX (where X is a numerical value) for the first two weeks and to Y (whereY is a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan or a variety of possible treatment plansfor selection by a healthcare provider that provides the result.Accordingly, when the data 600 for a new patient is input into thetrained machine learning model 13, the trained machine learning model 13may match the characteristics included in the data 600 withcharacteristics in either cohort A or cohort B and output theappropriate treatment plan or plans 602. In some embodiments, themachine learning model 13 may be trained to output one or more excludedtreatment plans that should not be performed by the new patient.

FIG. 7 generally illustrates an embodiment of an overview display 120 ofthe assistant interface 94 presenting recommended treatment plans andexcluded treatment plans in real-time during a telemedicine sessionaccording to the present disclosure. As depicted, the overview display120 just includes sections for the patient profile 130 and the videofeed display 180, including the self-video display 182. Any suitableconfiguration of controls and interfaces of the overview display 120described with reference to FIG. 5 may be presented in addition to orinstead of the patient profile 130, the video feed display 180, and theself-video display 182.

The healthcare professional using the assistant interface 94 (e.g.,computing device) during the telemedicine session may be presented inthe self-video 182 in a portion of the overview display 120 (e.g., userinterface presented on a display screen 24 of the assistant interface94) that also presents a video from the patient in the video feeddisplay 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe healthcare professional to share, in real-time or near real-timeduring the telemedicine session, the recommended treatment plans and/orthe excluded treatment plans with the patient on the patient interface50. The healthcare professional may select the GUI object 700 to sharethe recommended treatment plans and/or the excluded treatment plans. Asdepicted, another portion of the overview display 120 includes thepatient profile display 130.

The patient profile display 130 is presenting two example recommendedtreatment plans 600 and one example excluded treatment plan 602. Asdescribed herein, the treatment plans may be recommended in view ofcharacteristics of the patient being treated. To generate therecommended treatment plans 600 the patient should follow to achieve adesired result, a pattern between the characteristics of the patientbeing treated and a cohort of other people who have used the exerciseapparatus 70 to perform a treatment plan may be matched by one or moremachine learning models 13 of the artificial intelligence engine 11.Each of the recommended treatment plans may be generated based ondifferent desired results.

For example, as depicted, the patient profile display 130 presents “Thecharacteristics of the patient match characteristics of users in CohortA. The following treatment plans are recommended for the patient basedon his characteristics and desired results.” Then, the patient profiledisplay 130 presents recommended treatment plans from cohort A, and eachtreatment plan provides different results.

As depicted, treatment plan “A” indicates “Patient X should usetreatment apparatus for 30 minutes a day for 4 days to achieve anincreased range of motion of Y %; Patient X has Type 2 Diabetes; andPatient X should be prescribed medication Z for pain management duringthe treatment plan (medication Z is approved for people having Type 2Diabetes).” Accordingly, the treatment plan generated achievesincreasing the range of motion of Y %. As may be appreciated, thetreatment plan also includes a recommended medication (e.g., medicationZ) to prescribe to the patient to manage pain in view of a known medicaldisease (e.g., Type 2 Diabetes) of the patient. That is, the recommendedpatient medication not only does not conflict with the medical conditionof the patient but thereby improves the probability of a superiorpatient outcome. This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending multiple medications, or from handling theacknowledgement, view, diagnosis and/or treatment of comorbid conditionsor diseases.

Recommended treatment plan “B” may specify, based on a different desiredresult of the treatment plan, a different treatment plan including adifferent treatment protocol for an exercise apparatus 70, a differentmedication regimen, etc.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 602. These types of treatment plans are shownto the healthcare professional using the assistant interface 94 to alertthe healthcare professional not to recommend certain portions of atreatment plan to the patient. For example, the excluded treatment plancould specify the following: “Patient X should not use treatmentapparatus for longer than 30 minutes a day due to a heart condition;Patient X has Type 2 Diabetes; and Patient X should not be prescribedmedication M for pain management during the treatment plan (in thisscenario, medication M can cause complications for people having Type 2Diabetes). Specifically, the excluded treatment plan points out alimitation of a treatment protocol where, due to a heart condition,Patient X should not exercise for more than 30 minutes a day. Theruled-out treatment plan also points out that Patient X should not beprescribed medication M because it conflicts with the medical conditionType 2 Diabetes.

The healthcare professional may select the treatment plan for thepatient on the overview display 120. For example, the healthcareprofessional may use an input peripheral (e.g., mouse, touchscreen,microphone, keyboard, etc.) to select from the treatment plans 600 forthe patient. In some embodiments, during the telemedicine session, thehealthcare professional may discuss the pros and cons of the recommendedtreatment plans 600 with the patient.

In any event, the healthcare professional may select the treatment planfor the patient to follow to achieve the desired result. The selectedtreatment plan may be transmitted to the patient interface 50 forpresentation. The patient may view the selected treatment plan on thepatient interface 50. In some embodiments, the healthcare professionaland the patient may discuss during the telemedicine session the details(e.g., treatment protocol using treatment apparatus 70, diet regimen,medication regimen, etc.) in real-time or in near real-time. In someembodiments, the server 30 may control, based on the selected treatmentplan and during the telemedicine session, the exercise apparatus 70 asthe user uses the exercise apparatus 70.

FIG. 8 generally illustrates an embodiment of the overview display 120of the assistant interface 94 presenting, in real-time during atelemedicine session, recommended treatment plans that have changed as aresult of patient data changing according to the present disclosure. Asmay be appreciated, the exercise apparatus 70 and/or any computingdevice (e.g., patient interface 50) may transmit data while the patientuses the exercise apparatus 70 to perform a treatment plan. The data mayinclude updated characteristics of the patient and/or other treatmentdata. For example, the updated characteristics may include newperformance information and/or measurement information. The performanceinformation may include a speed of a portion of the exercise apparatus70, a range of motion achieved by the patient, a force exerted on aportion of the exercise apparatus 70, a heartrate of the patient, ablood pressure of the patient, a respiratory rate of the patient, and soforth.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is on track for the currenttreatment plan. Determining the patient is on track for the currenttreatment plan may cause the trained machine learning model 13 to adjusta parameter of the exercise apparatus 70. The adjustment may be based ona next step of the treatment plan to further improve the performance ofthe patient.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is not on track (e.g., behindschedule, not able to maintain a speed, not able to achieve a certainrange of motion, is in too much pain, etc.) for the current treatmentplan or is ahead of schedule (e.g., exceeding a certain speed,exercising longer than specified with no pain, exerting more than aspecified force, etc.) for the current treatment plan. The trainedmachine learning model 13 may determine that the characteristics of thepatient no longer match the characteristics of the patients in thecohort to which the patient is assigned. Accordingly, the trainedmachine learning model 13 may reassign the patient to another cohortthat includes qualifying characteristics the patient's characteristics.As such, the trained machine learning model 13 may select a newtreatment plan from the new cohort and control, based on the newtreatment plan, the exercise apparatus 70.

In some embodiments, prior to controlling the exercise apparatus 70, theserver 30 may provide the new treatment plan 800 to the assistantinterface 94 for presentation in the patient profile 130. As depicted,the patient profile 130 indicates “The characteristics of the patienthave changed and now match characteristics of users in Cohort B. Thefollowing treatment plan is recommended for the patient based on hischaracteristics and desired results.” Then, the patient profile 130presents the new treatment plan 800 (“Patient X should use the exerciseapparatus 70 for 10 minutes a day for 3 days to achieve an increasedrange of motion of L %” The healthcare professional may select the newtreatment plan 800, and the server 30 may receive the selection. Theserver 30 may control the exercise apparatus 70 based on the newtreatment plan 800. In some embodiments, the new treatment plan 800 maybe transmitted to the patient interface 50 such that the patient mayview the details of the new treatment plan 800.

In some embodiments, the server 30 described herein may be configuredfor optimizing at least one exercise for a user. An exercise apparatusmay be configured to enable the user to perform the at least oneexercise. In some embodiments, the server 30 described herein may beconfigured to receive user data. The user data may include attributedata associated with the user and outcome data associated with theexercise. The outcome data may be based on a selection by the user. Theoutcome data may be generated, based on the machine learning model, bythe artificial intelligence engine. The attribute of the user maycomprise at least one of a measurement of a vital sign of the user, arespiration rate of the user, a heartrate of the user, a heart rhythm ofa user, an oxygen saturation of the user, a sugar level of the user, acomposition of blood of the user, cerebral activity of the user,cognitive activity of the user, a lung capacity of the user, atemperature of the user, a blood pressure of the user, an eye movementof the user, a degree of dilation of an eye of the user, a reactiontime, a sound produced by the user, a perspiration rate of the user, anelapsed time of using the exercise apparatus, an amount of force exertedon a portion of the exercise apparatus, a range of motion achieved onthe exercise apparatus, a movement speed of a portion of the exerciseapparatus, a pressure exerted on a portion of the exercise apparatus, amovement acceleration of a portion of the exercise apparatus, a movementjerk of a portion of the exercise apparatus, a torque level of a portionof the exercise apparatus, and an indication of a plurality of painlevels experienced by the user when using the exercise apparatus.

In some embodiments, the server 30 described herein may be configured togenerate, based on the user data, initial target data. The initialtarget data may be associated with at least one of the user, theexercise apparatus, and the exercise.

In some embodiments, the server 30 described herein may be configured toreceive measurement data associated with at least one of the user, theexercise apparatus, and the exercise. The measurement data may beassociated with one or more sensors. The measurement data may be sensordata received from one or more sensors associated with at least one ofthe user, the exercise apparatus, and the exercise. The measurement datamay be received in real-time or near real-time. The outcome data mayinclude at least one of a duration of the exercise, a duration ofuninterrupted use, a weight, a number of repetitions, a respiration rateof the user, a heartrate of the user, a reaction time, a perspirationrate of the user, an amount of force exerted on a portion of theexercise apparatus, a range of motion achieved on the exerciseapparatus, a pressure exerted on a portion of the exercise apparatus, amovement speed of a portion of the exercise apparatus, a movementacceleration of a portion of the exercise apparatus, a movement jerk ofa portion of the exercise apparatus, a torque level of a portion of theexercise apparatus, or any combination thereof.

In some embodiments, the server 30 described herein may be configured todetermine differential data. The determining may be based on one or moredifferences between the initial target data and the measurement data. Insome embodiments, the server 30 described herein may be configured toreceive, based on cohort users who perform the exercise, cohort data.

In some embodiments, the server 30 described herein may be configured togenerate, via an artificial intelligence engine and based on thedifferential data, a machine learning model trained to generate messagedata based on a difference between the differential data and the cohortdata. The message data may comprise at least one of audio data, visualdata, and haptic data.

The audio data may include a verbal characteristic associated with atleast one of a volume, a cadence, a tone, an enunciation, a word, alanguage, a dialect, a vernacular, an accent, an emphasis, a pitch, arhythm, an order of words, a tense, a timbre, and a prosody. The verbalcharacteristic may be based on at least one of the cohort data and theoutcome data. The visual data may include a visual characteristicassociated with at least one of a color, an image, a video, a text, afont type, a font style, a point size, a font modifier, avirtual-reality environment, and an illumination. The visualcharacteristic may be based on at least one of the cohort data and theoutcome data. The haptic data may include a haptic characteristicassociated with at least one of a vibration, a force, a pressure, atorque, an intensity, a resistance, an electric stimulus, an ultrasonicfrequency, a heat level, and a temperature. The haptic characteristicmay be based on at least one of the cohort data and the outcome data.

It should be appreciated that, according to some embodiments,optimization of the at least one exercise is achieved by motivating viapositive or negative feedback, the user of the exercise apparatus 70.This may be accomplished via the particular message that is transmittedto the interface. For example, if the user is partially blind, themessage may not include textual elements, but rather will include anaudio and haptic element in order to alert the user to his status. Inthis particular example, if the user is pedaling the exercise apparatus70 too slowly according to the measurement data relative to the outcomedata, the message transmitted may audibly say, in a deep intense voice“Keep going, you can do it!” Alternatively, if the user has hearingtrouble, the message may instead output on the interface a bolded andunderlined textual message of similar terms. Additionally oralternatively, the user interface may display a red color with flashingelements, or alternatively may display an image of an avatar speakingthe textual message. In some embodiments, a video message may bedisplayed on the interface with the video including an avatar teachingthe user how to increase efficiency of the exercise.

It should further be appreciated that, according to some embodiments,optimization of the at least one exercise is achieved by monitoring theresponse of the user after the message is transmitted to the interface.In other words, in some embodiments, not only does the user receive themost optimal message available in the system, but the optimal message iscontinuously updated based on the response the user has to the optimalmessage. As such, the message may be actively refined over time with themachine learning model 13 being trained based on the response times ofprevious users with various conditions. For example, based on cohortdata, the machine learning model 13 may identify via correlation thatcertain conditions of a user produce certain measurement dataconsistently, despite the correlation being unnoticed or undetectable bya human observer. In addition, the machine learning model 13 mayidentify specific types of feedback in the message that are likely toinduce a particular response by the user. Unexpected responses to themessage may further allow the machine learning model 13 to try differentforms of feedback to identify another condition of the user. Forexample, if it is determined that the best message to output is anaudible one with a high degree of intensity, but outputting that messagedoes not achieve the desired outcome, the machine learning model maydetect that the user may suffer from hearing loss.

In some embodiments, the at least one exercise, including theconfigurations, settings, range of motion settings, pain level, forcesettings, and speed settings, etc. of the exercise apparatus 70 forvarious exercises, may be transmitted to the controller of the exerciseapparatus 70. In one example, if the user provides an indication, viathe patient interface 50, that he is experiencing a high level of painat a particular range of motion, the controller may receive theindication. Based on the indication, the controller may electronicallyadjust the range of motion of the pedal 102 by adjusting the pedalinwardly, outwardly, or along or about any suitable axis, via one ormore actuators, hydraulics, springs, electric motors, or the like. Theat least one exercise may define alternative range of motion settingsfor the pedal 102 when the user indicates certain pain levels during anexercise. Accordingly, once the at least one exercise is uploaded to thecontroller of the exercise apparatus 70, the exercise apparatus 70 maycontinue to operate without further instruction, further external input,and the like. It should be noted that the user (via the patientinterface 50) and/or the assistant (via the assistant interface 94) mayoverride any of the configurations or settings of the exercise apparatus70 at any time. For example, the user may use the patient interface 50to cause the exercise apparatus 70 to immediately stop, if so desired.

With reference to FIG. 9 , a method 900 of the present disclosure maycomprise the step 902 of receiving first patient data, wherein the firstpatient data may include at least a first patient identifier associatedwith the first patient and a first treatment plan. The patientidentifiers may each comprise at least one of a measurement of a vitalsign of patient, a respiration rate of the patient, a heartrate of thepatient, a heart rhythm of a patient, an oxygen saturation of thepatient, a sugar level of the patient, a composition of blood of thepatient, a cerebral activity of the patient, a cognitive activity of thepatient, a lung capacity of the patient, a temperature of the patient, ablood pressure of the patient, an eye movement of the patient, a degreeof dilation of an eye of the patient, a reaction time, a sound producedby the patient, a perspiration rate of the patient, an elapsed time forusing the exercise apparatus, an amount of force exerted on a portion ofthe exercise apparatus, a range of motion achieved on the exerciseapparatus, a speed of a portion of the exercise apparatus, a pressureexerted on a portion of the exercise apparatus, an acceleration of aportion of the exercise apparatus, a torque exerted to a portion of theexercise apparatus, and an indication of a pain level experienced by thepatient. Each of the patient identifiers may also be associated with aperformance level associated with a prior treatment plan.

The method 900 may comprise the step 904 of receiving second patientdata, wherein the second patient data may include both a second patientidentifier associated with the second patient and a second treatmentplan. The method 900 may comprise the step 906 of receiving firstmeasurement data associated with a first performance level of the firsttreatment plan by the first patient. The first and the secondperformance levels may comprise at least one of a measurement of a vitalsign of patient, a respiration rate of the patient, a heartrate of thepatient, a heart rhythm of a patient, an oxygen saturation of thepatient, a sugar level of the patient, a composition of blood of thepatient, a cerebral activity of the patient, a cognitive activity of thepatient, a lung capacity of the patient, a temperature of the patient, ablood pressure of the patient, an eye movement of the patient, a degreeof dilation of an eye of the patient, a reaction time, a sound producedby the patient, a perspiration rate of the patient, an elapsed time ofusing the exercise apparatus, an amount of force exerted on a portion ofthe exercise apparatus, a range of motion achieved on the exerciseapparatus, a speed of a portion of the exercise apparatus, a pressureexerted on a portion of the exercise apparatus, a movement accelerationof a portion of the exercise apparatus, a torque exerted to a portion ofthe exercise apparatus, and an indication of a pain level experienced bythe patient. The performance levels may each be measured relative to atleast one of the first and the second exercises or at least one priorexercise associated with the patient.

The method 900 may comprise the step 910 of receiving second measurementdata associated with a second performance level of the second treatmentplan by the second patient. The method 900 may comprise the step 912 ofdetermining differential data, wherein the determining is based on acontrast of one or more of the first and the second measurement data andfirst and second patient data. The method 900 may comprise the step ofgenerating, based on the differential data, an instruction to modify anoperating state of the treatment plan apparatus.

The method 900 of the disclosure may also comprise the step ofcontrolling, based on the instruction, at least one of the first and thesecond exercise apparatus. The controlling may comprise at leastselecting one of the first and the second exercise apparatus andmodifying an operating state of the exercise apparatus.

FIG. 11 shows an example computer system 1300, which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 1300may include a computing device and correspond to the assistanceinterface 94, reporting interface 92, supervisory interface 90,clinician interface 20, server 30 (including the AI engine 11), patientinterface 50, ambulatory sensor 82, goniometer 84, treatment apparatus70, pressure sensor 86, or any suitable component of FIG. 1 . Thecomputer system 1300 may be capable of executing instructionsimplementing the one or more machine learning models 13 of theartificial intelligence engine 11 of FIG. 1 . The computer system may beconnected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network. The computer system may operate in the capacity ofa server in a client-server network environment. The computer system maybe a personal computer (PC), a tablet computer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a camera, a video camera, an Internet of Things (IoT)device, or any device capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatdevice. Further, while only a single computer system is illustrated, theterm “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methods discussedherein.

The computer system 1300 includes a processing device 1302, a mainmemory 1304 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1306 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1308, which communicate with each other via a bus 1310.

Processing device 1302 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1302 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1302 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 102 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1300 may further include a network interface device1312. The computer system 1300 also may include a video display 1314(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1316 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1318 (e.g., a speaker).In one illustrative example, the video display 1314 and the inputdevice(s) 1316 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1316 may include a computer-readable medium 1320on which the instructions 1322 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1322 may also reside, completely or at least partially, within the mainmemory 1304 and/or within the processing device 1302 during executionthereof by the computer system 1300. As such, the main memory 1304 andthe processing device 1302 also constitute computer-readable media. Theinstructions 1322 may further be transmitted or received over a networkvia the network interface device 1312.

While the computer-readable storage medium 1420 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

Clause 1. A method for performing, by two or more patients, a respectivetreatment plan with respective first and second exercise apparatuses,the method comprising: receiving first patient data, wherein the firstpatient data includes at least a first patient identifier associatedwith the first patient and a first treatment plan receiving secondpatient data, wherein the second patient data includes a second patientidentifier associated with the second patient and a second treatmentplan; receiving first measurement data associated with a firstperformance level of the first treatment plan by the first patient;receiving second measurement data associated with a second performancelevel of the second treatment plan by the second patient; determiningdifferential data, wherein the determining is based on a contrast of oneor more of the first and the second measurement data and first andsecond patient data; and generating, based on the differential data, aninstruction to modify an operating state of the treatment apparatus.

Clause 2. The method of Clause 1, further comprising controlling, basedon the instruction, at least one of the first and the second exerciseapparatus.

Clause 3. The method of Clause 2, wherein controlling at least one ofthe first and the second exercise apparatus, comprises modifying anoperating state of the exercise apparatus.

Clause 4. The method of Clause 1, wherein the patient identifiers eachcomprise at least one of a measurement of a vital sign of patient, arespiration rate of the patient, a heartrate of the patient, a heartrhythm of a patient, an oxygen saturation of the patient, a sugar levelof the patient, a composition of blood of the patient, a cerebralactivity of the patient, a cognitive activity of the patient, a lungcapacity of the patient, a temperature of the patient, a blood pressureof the patient, an eye movement of the patient, a degree of dilation ofan eye of the patient, a reaction time, a sound produced by the patient,a perspiration rate of the patient, an elapsed time of using theexercise apparatus, an amount of force exerted on a portion of theexercise apparatus, a range of motion achieved on the exerciseapparatus, a speed of a portion of the exercise apparatus, a pressureexerted on a portion of the exercise apparatus, an acceleration of aportion of the exercise apparatus, a torque exerted to a portion of theexercise apparatus, and an indication of a plurality of pain levelsexperienced by the patient when using the exercise apparatus.

Clause 5. The method of Clause 4, wherein the patient identifiers areeach associated with a prior exercise performed by the first and secondpatient.

Clause 6. The method of Clause 5, wherein the patient identifier areeach associated with a performance level associated with a priortreatment plan.

Clause 7. The method of Clause 1, wherein each of the first and thesecond performance levels comprise at least one of a patient identifierseach comprise at least one of a measurement of a vital sign of patient,a respiration rate of the patient, a heartrate of the patient, a heartrhythm of a patient, an oxygen saturation of the patient, a sugar levelof the patient, a composition of blood of the patient, a cerebralactivity of the patient, a cognitive activity of the patient, a lungcapacity of the patient, a temperature of the patient, a blood pressureof the patient, an eye movement of the patient, a degree of dilation ofan eye of the patient, a reaction time, a sound produced by the patient,a perspiration rate of the patient, an elapsed time of using theexercise apparatus, an amount of force exerted on a portion of theexercise apparatus, a range of motion achieved on the exerciseapparatus, a speed of a portion of the exercise apparatus, a pressureexerted on a portion of the exercise apparatus, an acceleration of aportion of the exercise apparatus, a torque exerted to a portion of theexercise apparatus, and an indication of a plurality of pain levelsexperienced by the patient when using the exercise apparatus.

Clause 8. The method of Clause 7, wherein the performance levels areeach measured relative to at least one of the first and the secondexercises.

Clause 9. The method of Clause 8, wherein the first and the secondperformance levels are each measured relative to at least one priorexercise.

Clause 10. The method of Clause 9, wherein the first and the secondperformance levels are measured relative to at least one prior exerciseassociated with at least one of the first and the second patient.

Clause 11. A system for performing, by two or more patients, exerciseswith an exercise apparatus, the system comprising: a processing device;an artificial intelligence engine communicatively coupled to theprocessing device; a memory including instruction that, when executed bythe processing device, cause the processing device to: receive firstpatient data, wherein the first patient data includes at least a firstpatient identifier associated with the first patient and a firsttreatment plan; receive second patient data, wherein the second patientdata includes a second patient identifier associated with the secondpatient and a second treatment plan; receive first measurement dataassociated with a first performance level of the first treatment plan bythe first patient; receive second measurement data associated with asecond performance level of the second exercise by the second patient;receive second measurement data associated with a second performancelevel of the second treatment plan by the second patient; determine, viathe artificial intelligence engine and based on a contrast of one ormore of the first and the second measurement data and first and secondpatient data, differential data; and generate, via the artificialintelligence engine and based on the differential data, an instructionto modify at least one of the first and the second exercises.

Clause 12. The system of Clause 11, further comprised of control, basedon the differential data, at least one of the first and the secondexercise apparatus.

Clause 13. The system of Clause 12, wherein the control of the at leastone of the first and the second exercise apparatus, comprises modifyingan operating state of the exercise apparatus.

Clause 14. The system of Clause 11, wherein the patient identifiers eachcomprise at least one of a measurement of a vital sign of patient, arespiration rate of the patient, a heartrate of the patient, a heartrhythm of a patient, an oxygen saturation of the patient, a sugar levelof the patient, a composition of blood of the patient, a cerebralactivity of the patient, a cognitive activity of the patient, a lungcapacity of the patient, a temperature of the patient, a blood pressureof the patient, an eye movement of the patient, a degree of dilation ofan eye of the patient, a reaction time, a sound produced by the patient,a perspiration rate of the patient, an elapsed time of using theexercise apparatus, an amount of force exerted on a portion of theexercise apparatus, a range of motion achieved on the exerciseapparatus, a movement speed of a portion of the exercise apparatus, apressure exerted on a portion of the exercise apparatus, a movementacceleration of a portion of the exercise apparatus, a movement jerk ofa portion of the exercise apparatus, a torque level of a portion of theexercise apparatus, and an indication of a plurality of pain levelsexperienced by the patient when using the exercise apparatus.

Clause 15. The system of Clause 14, wherein the patient identifiers areeach associated with a prior exercise performed by the patient.

Clause 16. The system of Clause 15, wherein the patient identifiers areeach associated with a performance level associated with a priorexercise.

Clause 17. The system of Clause 11, wherein each of the first and thesecond performance levels comprise at least one of a patient identifierseach comprise at least one of a measurement of a vital sign of patient,a respiration rate of the patient, a heartrate of the patient, a heartrhythm of a patient, an oxygen saturation of the patient, a sugar levelof the patient, a composition of blood of the patient, a cerebralactivity of the patient, a cognitive activity of the patient, a lungcapacity of the patient, a temperature of the patient, a blood pressureof the patient, an eye movement of the patient, a degree of dilation ofan eye of the patient, a reaction time, a sound produced by the patient,a perspiration rate of the patient, an elapsed time of using theexercise apparatus, an amount of force exerted on a portion of theexercise apparatus, a range of motion achieved on the exerciseapparatus, a speed of a portion of the exercise apparatus, a pressureexerted on a portion of the exercise apparatus, an acceleration of aportion of the exercise apparatus, a torque exerted to a portion of theexercise apparatus, and an indication of a plurality of pain levelsexperienced by the patient when using the exercise apparatus.

Clause 18. The system of Clause 17, wherein the first and the secondperformance levels are measured relative to at least one of the firstand the second exercises.

Clause 19. The system of Clause 18, wherein the first and the secondperformance levels are measured relative to at least one prior exercise.

Clause 20. The system of Clause 19, wherein the first and the secondperformance levels are measure relative to at least one prior exerciseof at least one of the first and the second patient.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

What is claimed is:
 1. A method for performing, by two or more patients,a respective treatment plan with respective first and second exerciseapparatuses, the method comprising: receiving first patient data,wherein the first patient data includes at least a first patientidentifier associated with the first patient and a first treatment plan;receiving second patient data, wherein the second patient data includesa second patient identifier associated with the second patient and asecond treatment plan; receiving first measurement data associated witha first performance level of the first treatment plan by the firstpatient; receiving second measurement data associated with a secondperformance level of the second treatment plan by the second patient;determining differential data, wherein the determining is based on acontrast of one or more of the first and the second measurement data andfirst and second patient data; and generating, based on the differentialdata, an instruction to modify an operating state of the treatmentapparatus.
 2. The method of claim 1, further comprising controlling,based on the instruction, at least one of the first and the secondexercise apparatus.
 3. The method of claim 2, wherein controlling atleast one of the first and the second exercise apparatus, comprisesmodifying an operating state of the exercise apparatus.
 4. The method ofclaim 1, wherein the patient identifiers each comprise at least one of ameasurement of a vital sign of patient, a respiration rate of thepatient, a heartrate of the patient, a heart rhythm of a patient, anoxygen saturation of the patient, a sugar level of the patient, acomposition of blood of the patient, a cerebral activity of the patient,a cognitive activity of the patient, a lung capacity of the patient, atemperature of the patient, a blood pressure of the patient, an eyemovement of the patient, a degree of dilation of an eye of the patient,a reaction time, a sound produced by the patient, a perspiration rate ofthe patient, an elapsed time for using the exercise apparatus, an amountof force exerted on a portion of the exercise apparatus, a range ofmotion achieved on the exercise apparatus, a movement speed of a portionof the exercise apparatus, a pressure exerted on a portion of theexercise apparatus, a movement acceleration of a portion of the exerciseapparatus, a movement jerk of a portion of the exercise apparatus, atorque level of a portion of the exercise apparatus, and an indicationof a plurality of pain levels experienced by the patient when using theexercise apparatus.
 5. The method of claim 4, wherein the patientidentifiers are each associated with a prior exercise performed by thefirst and second patient.
 6. The method of claim 5, wherein the patientidentifier are each associated with a performance level associated witha prior treatment plan.
 7. The method of claim 1, wherein each of thefirst and the second performance levels comprise at least one of ameasurement of patient identifiers each comprise at least one of ameasurement of a vital sign of patient, a respiration rate of thepatient, a heartrate of the patient, a heart rhythm of a patient, anoxygen saturation of the patient, a sugar level of the patient, acomposition of blood of the patient, a cerebral activity of the patient,a cognitive activity of the patient, a lung capacity of the patient, atemperature of the patient, a blood pressure of the patient, an eyemovement of the patient, a degree of dilation of an eye of the patient,a reaction time, a sound produced by the patient, a perspiration rate ofthe patient, an elapsed time of using the exercise apparatus, an amountof force exerted on a portion of the exercise apparatus, a range ofmotion achieved on the exercise apparatus, a speed of a portion of theexercise apparatus, a pressure exerted on a portion of the exerciseapparatus, an acceleration of a portion of the exercise apparatus, atorque exerted to a portion of the exercise apparatus, and an indicationof a plurality of pain levels experienced by the patient when using theexercise apparatus.
 8. The method of claim 7, wherein the performancelevels are each measured relative to at least one of the first and thesecond exercises.
 9. The method of claim 8, wherein the first and thesecond performance levels are each measured relative to at least oneprior exercise.
 10. The method of claim 9, wherein the first and thesecond performance levels are measured relative to at least one priorexercise associated with at least one of the first and the secondpatient.
 11. A system for performing, by two or more patients, exerciseswith an exercise apparatus, the system comprising: a processing device;an artificial intelligence engine communicatively coupled to theprocessing device; a memory including instruction that, when executed bythe processing device, cause the processing device to: receive firstpatient data, wherein the first patient data includes at least a firstpatient identifier associated with the first patient and a firsttreatment plan; receive second patient data, wherein the second patientdata includes a second patient identifier associated with the secondpatient and a second treatment plan; receive first measurement dataassociated with a first performance level of the first treatment plan bythe first patient; receive second measurement data associated with asecond performance level of the second exercise by the second patient;receive second measurement data associated with a second performancelevel of the second treatment plan by the second patient; determine, viathe artificial intelligence engine and based on a contrast of one ormore of the first and the second measurement data and first and secondpatient data, differential data; and generate, via the artificialintelligence engine and based on the differential data, an instructionto modify at least one of the first and the second exercises.
 12. Thesystem of claim 11, further comprised of control, based on thedifferential data, at least one of the first and the second exerciseapparatus.
 13. The system of claim 12, wherein the control of the atleast one of the first and the second exercise apparatus, comprisesmodifying an operating state of the exercise apparatus.
 14. The systemof claim 11, wherein the patient identifiers each comprise at least oneof a measurement of a vital sign of patient, a respiration rate of thepatient, a heartrate of the patient, a heart rhythm of a patient, anoxygen saturation of the patient, a sugar level of the patient, acomposition of blood of the patient, a cerebral activity of the patient,a cognitive activity of the patient, a lung capacity of the patient, atemperature of the patient, a blood pressure of the patient, an eyemovement of the patient, a degree of dilation of an eye of the patient,a reaction time, a sound produced by the patient, a perspiration rate ofthe patient, an elapsed time of using the exercise apparatus, an amountof force exerted on a portion of the exercise apparatus, a range ofmotion achieved on the exercise apparatus, a speed of a portion of theexercise apparatus, a pressure exerted on a portion of the exerciseapparatus, an acceleration of a portion of the exercise apparatus, atorque exerted to a portion of the exercise apparatus, and an indicationof a plurality of pain levels experienced by the patient when using theexercise apparatus.
 15. The system of claim 14, wherein the patientidentifiers are each associated with a prior exercise performed by thepatient.
 16. The system of claim 15, wherein the patient identifiers areeach associated with a performance level associated with a priorexercise.
 17. The system of claim 11, wherein each of the first and thesecond performance levels comprise at least one of a measurement of avital sign of patient, a respiration rate of the patient, a heartrate ofthe patient, a heart rhythm of a patient, an oxygen saturation of thepatient, a sugar level of the patient, a composition of blood of thepatient, a cerebral activity of the patient, a cognitive activity of thepatient, a lung capacity of the patient, a temperature of the patient, ablood pressure of the patient, an eye movement of the patient, a degreeof dilation of an eye of the patient, a reaction time, a sound producedby the patient, a perspiration rate of the patient, an elapsed time ofusing the exercise apparatus, an amount of force exerted on a portion ofthe exercise apparatus, a range of motion achieved on the exerciseapparatus, a movement speed of a portion of the exercise apparatus, apressure exerted on a portion of the exercise apparatus, a movementacceleration of a portion of the exercise apparatus, a movement jerk ofa portion of the exercise apparatus, a torque level of a portion of theexercise apparatus, and an indication of a plurality of pain levelsexperienced by the patient when using the exercise apparatus.
 18. Thesystem of claim 17, wherein the first and the second performance levelsare measured relative to at least one of the first and the secondexercises.
 19. The system of claim 18, wherein the first and the secondperformance levels are measured relative to at least one prior exercise.20. The system of claim 19, wherein the first and the second performancelevels are measure relative to at least one prior exercise of at leastone of the first and the second patient.